1. Field of the Invention
The present invention relates to the field of segmentation of medical digital images, and more particularly to lung lobe segmentation.
2. Prior Art
The human lungs are composed of distinct anatomic compartments called lobes. Since they represent functional units, the lobes have their own bronchial and vascular systems. These systems are solely connected close to the hilus, which is defined as the area where bronchi as well as the pulmonary arteries and veins enter the lungs. The lobes in each lung are separated by thin tissue, the so-called lobar fissures. An oblique fissure and a horizontal fissure divide the right lung into upper, lower, and middle lobe. In the left hand lung there is only an oblique fissure separating lower and upper lobe.
High-resolution X-ray computer tomography (CT) is the standard for pulmonary imaging. Depending on the scanner hardware, CT can provide high spatial and high temporal resolution, excellent contrast resolution for the pulmonary structures and surrounding anatomy, and the ability to gather a complete three-dimensional (3-D) volume of the human thorax in a single breath hold (E A Hoffman and G McLennan, “Assessment of the pulmonary structure-function relationship and clinical outcomes measures: Quantitative volumetric CT of the lung,” Academic Radiol., vol. 4, no. 11, pp. 758-776, 1977).
Pulmonary CT images have been used for applications such as lung parenchyma density analysis (L W Hedlund, R F Anderson, P L Goulding, J W Beck, E L Eff-mann, and C E Putman, “Two methods for isolating the lung area of a CT scan for density information,” Radiology, vol. 144, pp. 353-357, 1982, R Uppaluri, T Mitsa, M Sonka, E A Hoffman, and G McLennan, “Quantification of pulmonary emphysema from lung CT images using texture analysis,” Amer J Resp Crit Care Med., vol. 156, no. 1, pp. 248-254, 1997), airway analysis (I Amirav, S. S. Kramer, M. M. Grunstein, and E. A. Hoffman, “Assessment of methacholine-induced airway constriction by ultrafast high-resolution computer tomography,” J Appl. Physiol., vol. 144, no. 1, pp. 208-212, 1991; R H Brown, C J Herold, C A Hirshman, E A Zerhouni, and W Mitzner, “In vivo measurment of airway reactivity using high resolution computed tomography,” Amer. Rev. Resp. Dis., vol. 144, no. 1, pp. 208-212, 1991), and lung and diaphragm mechanics analysis (E A Hoffman, T Behrenbeck, P A Chevalier, and E H Wood, “Estimation of regional pleural surface expansile forces in intact dogs,” J. Appl. Phsyiol., vol. 55, no. 3, pp. 935-948, 1983; A M Boriek, S Liu, and J R Rodarte, “Costal diaphragm curvature in the dog,” J. Appl. Physiol., vol. 75, no. 2, pp. 527-533, 1993). A precursor to all of these quantitative analysis applications is lung segmentation.
A number of techniques for computer-assisted segmentation of pulmonary CT images has been developed in the past. A fully automatic method for identifying the lungs in CT images is known from “Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images”, By, Hu, S., Hoffman, E. A., Reinhardt, J. M., Medical Imaging, IEEE Transactions, Pages 490 to 498, June 2001 Volume:20 Issue:6.
This method has three main steps:
First, the lung region is extracted from the CT images by grey-level thresholding. The left and right lungs are then separated by detecting the anterior and posterior junctions. Finally, the lung boundary is smoothed along the mediastinum.
Such prior art lung segmentation techniques do not take the the lung lobes into account and thus do not provide lobar quantitation support.
Further, the extraction of lobar fissures using morphological knowledge is as such known from L Zhang and J M Reinhardt, “Detection of lung lobar fissures using fuzzy logic,” in physiology and Function from Multidemensional Images,
L. Zhang, E. A. Hoffman, and J. M. Reinhardt, “Lung lobe segmentation by graph search with 3D shape constraints,” in Physiology and Function from Multidimensional Images, C.-T. Chen and A. V. Clough, eds., Proc. SPIE 4321, pp. 204-215, 2001, C.-T Chen and A V Clough, eds., Proc SPIE 3660, pp. 188-199, 1999; “Extraction of pulmonary fissures from thin-section CT images using calculation of surface-curvatures and morphology filters”, By, Kubo, M., Niki, N., Eguchi, K., Kaneko, M., Kusumoto, M., Moriyama, N., Omatsu, H., Kakinuma, R., Nishiyama, H., Mori, K., Yamaguchi, N. Image Processing, 2000. Proceedings. 2000 International Conference on Page(s): 637-640 vol. 2 2000 Volume:2. Here, the fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. Again only morphological knowledge is used.
From Krass et al: “A method for the determination of bronchopulmonary segments based on HRCT data”, International Congress Series, Excerpta Medica, Amsterdam, N L, vol. 1214, 28 Jun. 2000 (2000-06-28) pages 584-589, XP008019539 ISSN: 0531-5131 it is known to segment the lung parenchyma using a region growing algorithm. The segmentation of the bronchial tree is performed using a vessel tracing algorithm based on region growing. The segmentation of the bronchial tree is a prerequisite for the determination of the lung segments. To achieve this automatically, the structure and morphology of the vessel systems have to be analysed. For this purpose the tubular structure is skeletonized. For further analysis, the skeleton is interpreted as a graph, where the vertices represent ramification points of the medical axis and the edges represent the parts of the medical axis between the ramifications. The bronchi belonging to each lung lobe and to each segment are identified automatically using graph theoretical methods. For the prediction of the lung segments based on an incomplete bronchial tree, a nearest neighbour approximation is used in order to keep the computational costs low. Using this method, a segment is approximated as the set of voxels in the interior of the lung whose distance to the specific segment subtree is smaller than their distance to all other segment subtrees.
From Selle D. et al: “Analysis of vasculature for liver surgical planning” IEEE Transactions on Medical Imaging, November 2002, IEEE, USA, vol. 21, no 11, pages 1344-1357, XP002250046 ISSN: 0278-0062 a method for segmentation of the intrahepatic vessels for a subsequent geometrical and structural analysis is known.
Hahn H. K. et al: “The skull stripping problem in MRI solved by a single 3D watershed transform” Medical Image Computing and computer assisted Intervention—MICCAI 2000. Third International Conference. Proceedings (Lecture Notes in Computer Science Vol. 1935), pages 134-143, XP002250047 2000 Berlin, Germany, Springer-Verlag, Germany ISBN: 3-540-41189-5 shows a method for brain segmentation that is based on a three dimensional watershed transformation.
From Pratikakis I. E. et al: “Low level image partitioning guided by the gradient watershed hierarchy” Signal Processing, Amsterdam, N L, vol. 75, no. 2, June 1999 (1999-06), pages 173-195, XP004164346 ISSN: 0165-1684 an imaging partitioning method based on the construction of hierarchies for a gradient watershed is known.
The present invention therefore aims to provide an improved method of lung lobe segmentation and a corresponding computer program product and computer system.